Excess Stock Returns Research Articles

Asset pricing and FOMC press conferences

A press conference (pc) organized by the Federal Open Market Committee (fomc) followed half of the scheduled announcements from 2011 to 2018. We document that excess stock returns are strongly and positively related to their betas on announcement days with a pc. In addition, the cross-sectional dispersion in betas declines substantially on pc days when measured using both daily and intraday return data. These effects are absent on announcement days without a pc. Last, we find that stock-bond correlations are positive (negative) on pc (all other) days and that their variations are related to uncertainty and yield curve information. We discuss implications and possible explanations for our findings.

Unemployment and aggregate stock returns

This paper presents new evidence on the predictability of aggregate stock market returns by a labor market based economic indicator–the unemployment gap–which is defined as the difference between the actual unemployment rate and its estimated trend. The unemployment gap emerges as a strong predictor of both raw stock returns and excess stock returns, in the U.S. and in international data. In addition, fluctuations in the unemployment gap are tightly linked to cyclical variation in risk preferences. A widening of the unemployment gap, i.e. a rise in the unemployment rate relative to its trend, is associated with a rise in risk aversion and higher excess returns. The results support the view that the expected equity risk premiums arise as a rational response of economic agents to changes in the investment opportunities.

Improvements in forecasting insurance stock excess returns: Comparing the investor sentiment endurance index with the CAPM and Fama-French models

ABSTRACT This study applies the sentiment endurance (SE) index developed by He (2012) to forecast excess returns of insurance stocks. With the exception of the 12-month rolling forecasts of the Fama-French three-factor model (FF), forecasts of the SE model persistently outperform that of the CAPM and FF models in terms of lower absolute percent forecasting error (APFE) and significantly lower standard deviation of APFE. The accuracy of 6-month rolling forecasts of SE model is significantly higher than that of the FF model. Further, this study finds that the inclusion of SMB and HML in the SE model significantly deteriorates the accuracy and stability of forecasts. To a lesser degree, the addition of the market risk factor to the SE model hurts more than it improves the quality of forecasts. The results clearly suggest that compared to global variables, the SE index, as a local variable, more accurately reflects insurance investor sentiment and response to news and therefore can better forecast excess returns of insurance stocks.

How Does Google Search Affect the Stock Market? Evidence from Indian Companies

Google search data has received considerable attention for its predictive ability in various social and economic outcomes. In the arena of investments, a surge in online searches indicates an enhanced interest of investors, particularly retail, in that company. In this article, we have examined the association between Google search and stock prices in a sample of Indian companies. The results suggest that an increase in Google search is positively related to future excess stock returns, liquidity and volatility. The positive influence of Google search on stock prices, however, is temporary and reverses in the next week. We further show that the market sentiment moderates the interconnection between Google searches and future excess stock returns. The findings are in consonance with the ‘price pressure hypothesis’ of Barber and Odean (2008, Review of Financial Studies, 21(2), 785–818).

Can demographic structures help predict equity premiums?: Evidence from a panel with cross-section dependence

ABSTRACT This paper re-examines the link between equity risk premiums and demographic changes using panel data of 17 advanced countries over the postwar period. Based on the method of Pesaran (2006) that takes into account cross-section dependence, we find little evidence that demographic structures predict excess stock returns. However, demographic variables appear to have strong predictability of the excess returns in pooled OLS regressions. These results suggest that the failure of controlling for the influence of the common factors would lead to spurious estimation results.

The Impact of Herding on the Risk Pricing in the Egyptian Stock Exchange

We test the impact of herding behaviour on the risk pricing in the Egyptian Stock Exchange (EGX) by adding an additional risk factor reflecting herding behaviour to the Fama and French three-factor model. We construct a portfolio to mimic an additional risk factor related to herding behaviour, in addition to the original risk factors in the Fama and French three-factor model. The three-factor model will be tested in its original form and re-tested after adding the herding behaviour factor. The study is based on Hwang and Salmon methodology, in which the state space approach based on Kaman’s filter was used to measure herding behaviour. We used monthly excess stock returns of 50 stocks listed on the EGX from January 2014 to December 2018. The results do not support Fama and French model before and after adding the herding behaviour factor, therefore, there is no effect of herding behaviour on the risk pricing in the Egyptian Stock Exchange.

Empirical Test of Fama and French Three-Factor Model in the Egyptian Stock Exchange

We test the empirical validity of the three-factor model of Fama and French in the Egyptian Stock Exchange (EGX) using monthly excess stock returns of 50 stocks listed on the EGX from January 2014 to December 2018. Our findings do not support Fama and French three-factor model, where the coefficient of the beta was insignificant. The “SBM” coefficient and the “HML” coefficient were equal to zero and insignificant, which confirms the absence of the small firm effect and book-to-market ratio effect in the market. We conclude that there is no relation between expected return and Fama-French risk factors.

Liquidity Risks and Asset Pricing: Evidence from Developed and Emerging Markets

The study examines the liquidity adjusted capital asset pricing model in developed and emerging markets. Amihud measure is used to compute market liquidity. Innovations in Amihud ratio are generated through the autoregressive process to avoid autocorrelation in illiquidity data series. Decile portfolios based on illiquidity cost are formulated for each stock market. Liquidity adjusted betas are calculated at the portfolio level and then stocks as test assets have been used in the regression stage. Panel regression with fixed effect has been employed on LCAPM specifications for explaining the excess stock returns of developed and emerging markets during a period July 2005- June 2017. The findings of the study support that individual and aggregate liquidity risk price in stock markets except for Pakistan. The results of the study suggest that investors institutional or individual should consider liquidity risks for assessing the worth of assets.

COVID-19: Risk-adjusted portfolio returns of emerging and developed equity markets

This study examines the impact of the novel coronavirus (COVID-19) on the stock markets’ risk and return across emerging and developed countries. Based on a sample of 76 countries from 14th January through 19th August, 2020, the authors find that the stock excess returns are negatively related to daily new cases, but not deaths from COVID-19. The authors’ ex-post analysis indicates that the daily cases from COVID-19 are strongly related to daily stock excess returns in both emerging and developed markets during the declining period of the equity markets (pre 23rd March, 2020). Although the equity markets in the developed countries have experienced an unprecedented recovery during post 23rd March, 2020, the authors find that the stock markets in emerging countries exhibit greater positive abnormal returns above their respective market benchmarks. Findings from their ex-post analysis indicate that there is a portfolio diversification that could have been gleaned by investing in both emerging and developed equity markets during the post stock markets’ decline due to COVID-19.

Multilayer Network Risk Factor Pricing Model

This paper proposes a multilayer network risk factor pricing model to depict the impact of interactions between stocks on excess stock returns by constructing the network risk factor based on the stock multilayer network and introducing it to the traditional three-factor pricing model. According to China’s stock market data, we find that compared with the traditional three-factor model, the multilayer network risk factor pricing model can achieve higher fitting degree. Meanwhile, the multilayer network risk factor has a significant positive impact on the excess stock returns in most cases.

Predictability of GCC stock returns: The role of geopolitical risk and crude oil returns

Stock return predictability has always been one of the central themes of finance literature, given its crucial implications for investment decisions, risk management, and financial and monetary policymaking. This paper evaluates the in-sample and out-of-sample stock return predictive power of the global and Saudi geopolitical risk indices and crude oil returns in the context of six Gulf Cooperation Council (GCC) countries. Monthly data from February 2007 to December 2019 and the feasible generalized least square (FGLS) estimator for predictive modelling by Westerlund and Narayan (2012, 2015) are used. Global and Saudi GPR indices show weak evidence of in-sample predictability of excess stock returns. However, the out-of-sample forecasts show that only the global geopolitical risk index provides superior prediction in the context of Kuwaiti and Omani stock markets, compared to the historical average benchmark model. Crude oil prices are shown to be a better predictor in most cases, in both in-sample and out-of-sample forecast models The results imply that crude oil returns can be used for active prediction of GCC stock market returns, once econometric issues are accounted for. The findings remain mostly unaffected when excess risk adjusted returns are used.

Policy uncertainty in Australian financial markets

Economic policy touches most facets of corporate decision-making and variations in policy can elicit significant changes in financial performance and asset prices. We utilize the economic policy uncertainty (EPU) measure of Baker et al. to investigate the extent to which policy uncertainty influences Australian financial market returns. Our empirical results demonstrate that both domestic and global uncertainty have a significant negative impact on excess stock returns, changes in bond yields and Australian dollar (AUD) returns. The relationship is concentrated in the left tail of the return distribution and largely driven by increases in policy uncertainty. Although the identified relationship is negative throughout the sample period, the magnitude of the relationship appears to be state dependent and is influenced by periods of high uncertainty, recession and the lead-up to federal elections. The most plausible explanation for our results is that uncertainty about economic policy is channelled to financial markets via the discount rate effect, resulting in a higher risk premium. Our results are important for investors, corporate managers and policy makers wishing to navigate periods of policy uncertainty. JEL Classification: G10, G12, G14, G15

Shareholder Valuation on a Dollar of Cash Versus Agency Cost: Evidence from High-cash and Low-Cash Level Firm

The corporate cash holdings are important when deciding performance and profitability of firms, and also as an extension to shareholders’ wealth. The issue of the amount of cash holdings in firms does not solely impact the firms’ balance sheets and agency costs. This study argues that the need for holding more cash to reduce default probability is relatively more significant, and should be focused upon rather than agency cost. To compare the agency cost and the value of holing more cash in firm, this study employs the Faulkender & Wang (2006) methodology in estimating the cash value against excess stock returns, which reflects shareholders’ valuation. As the findings, firms with higher cash would generate higher excess stock returns. The shareholders’ valuation does not tally with agency cost, showing that shareholders prefer firms to hold less cash. Furthermore, the optimal cash level is not significantly related with shareholders’ valuation.

Nexus between corporate social responsibility and financial and non-financial sectors' performance: a non-linear and disaggregated approach.

The objective of this study is to examine the relationship between corporate social performance (CSP) as proxy of corporate social responsibility (CSR) and corporate firm's performance (CFP) in the context of Pakistani financial and non-financial firms sectors. This study comprises two main firm's performance indicators such as market base (excess stock returns) and accounting base (returns on assets and returns on capital). The data set starts from 2011 to 2017 and consists of three hundred and fifty (350) firms on equal numbers of financial and non-financial firms. This study uses a non-linear and disaggregated approach for data analysis. The results of the linear model indicate that CSP and returns on capital have a negative relationship, while the non-linear model of CSP and accounting base performance as CFP have positive association in the domain of long run. There is a significant relationship that exist among environmental social governance (ESG) disclosure score, government sub-components score, and social performance. However, a U-shaped association found between CFP and government sub-components, which further suggest that governance has a vital role toward CSP and CFP components.

Incorporating Research Reports and Market Sentiment for Stock Excess Return Prediction: A Case of Mainland China

The prediction of stock excess returns is an important research topic for quantitative trading, and stock price prediction based on machine learning is receiving more and more attention. This article takes the data of Chinese A-shares from July 2014 to September 2017 as the research object, and proposes a method of stock excess return forecasting that combines research reports and investor sentiment. The proposed method measures individual stocks released by analysts, separates the two indicators of research report attention and rating sentiment, calculates investor sentiment based on external market factors, and uses the LSTM model to represent the time series characteristics of stocks. The results show that (1) the accuracy and F1 evaluation indicators are used, and the proposed algorithm is better than the benchmark algorithm. (2) The performance of deep learning LSTM algorithm is better than traditional machine learning algorithm SVM. (3) Investor sentiment as the initial hidden state of the model can improve the accuracy of the algorithm. (4) The attention of the split research report takes the two indicators of investor sentiment and price as the input of the model, which can effectively improve the performance of the model.

Lumpy investment and expected stock returns

This study investigates the predictability of stock market returns using a novel corporate investment measure that captures the lumpiness of firm-level investment. We find that the proportion of firms with investment spikes (spike) is a strong predictor of excess stock returns. Specifically, an increase in spike significantly lowers future excess stock returns. The predictive ability of spike is consistently observed in both in-sample and out-of-sample tests. Furthermore, spike shows strong predictive ability at the business cycle frequency, suggesting that its predictive ability is driven by the time-varying risk premium associated with business cycles rather than temporary mispricing.

Small-sample tests for stock return predictability with possibly non-stationary regressors and GARCH-type effects

We develop a simulation-based procedure to test for stock return predictability with multiple regressors. The process governing the regressors is left completely free and the test procedure remains valid in small samples even in the presence of non-normalities and GARCH-type effects in the stock returns. The usefulness of the new procedure is demonstrated in a simulation study and by examining the ability of a group of financial variables to predict excess stock returns. We find some evidence of predictability during the period 1948–2014, driven entirely by the term spread. This empirical evidence, however, is much weaker over subsamples.

Compensation for illiquidity in China: Evidence from an alternative measure

This paper shows that liquidity is an important source of priced risk in China. Using A-share stocks in Shanghai and Shenzhen Exchange over the period 2007–2017, we examine the influence of liquidity on stock returns. A new liquidity measure that captures multiple dimensions of liquidity is proposed. Fama-Macbeth cross-sectional regression shows that the expected return is negatively correlated with liquidity. Based on Fama and French (1993), we propose a five-factor pricing model by incorporating reversal factor and liquidity factor. Time-series regressions show that the liquidity factor makes significantly marginal contributions to explaining excess stock returns. The liquidity factor based on the proposed measure works better than alternative liquidity measures such as turnover, Amihud illiquidity measure and the measure in Liu (2006).

Corporate social responsibility and cash holding: evidence from trucking firms

ABSTRACT This study analyses the influence of corporate social responsibility (CSR) on the corporate cash management of trucking firms. The fixed-effects model of the panel data analysis is applied from 2007 to 2017. Capital expenditures, dividend payments, and retained earnings negatively influence cash holdings, whereas cash flow, market-to-book ratio, net working capital, and share repurchase for all CSR categories positively influence cash holdings. However, the changes in market leverage, dividends, book-to-market ratio, and firm size negatively and significantly influence excess stock returns. In summary, social and environmental participations positively influence cash holdings and excess stock returns, respectively.

Institutional Industry Herding in China

This paper examines the effect of institutional herding on future stock returns in China A-share market at both the market and industry level from 2003 to 2012. Using a unique institutional holding database, we test the herding effect at different time horizons. The results suggest that institutional herding has a significantly positive effect on future excess returns for A shares in the short, medium and long periods of time. In China A-share market, institutional herding is more significant on buy side than sell side due to short sell restrictions. At the industry level, manufacturing and construction sectors experience institutional herding effect at all time horizon. Financial industry is found to present significant institutional herding effect only in the long term. The institutional herding has a positive and significant impact on the medium-term and long-term excess stock returns in the rest of ten sectors.